This repository is a collection of resources related to data engineering that I find valuable and worth sharing. It includes links to articles, books, case studies, communities, courses, daily reads, databases, diagrams, practical projects, tools, interview questions, and learning paths.
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Books: A list of recommended books on data engineering topics. Resources to deepen your understanding of data engineering concepts, best practices, and advanced techniques.
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Case Studies: Real-world case studies showcasing data engineering implementations and solutions. Learn from practical examples and successful data engineering projects across various industries.
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Communities: Online communities and forums where data engineers can connect, ask questions, and share insights. Engage with like-minded professionals, exchange ideas, and stay updated on the latest trends in data engineering.
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Concepts: Key concepts and fundamentals in data engineering explained. Understand the core principles behind data processing, storage, transformation, and analysis.
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Courses: Online courses and tutorials for learning data engineering skills. Structured learning paths to acquire both foundational knowledge and advanced expertise in data engineering.
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Daily Reads: Articles, blog posts, and papers related to data engineering that are worth reading. Stay informed about the latest developments, trends, and innovations in the field of data engineering.
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Databases: Different types of databases commonly used in data engineering projects and their features. Explore various database systems, including relational, NoSQL, and distributed databases, and their use cases.
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Diagrams: Visual diagrams and illustrations explaining data engineering concepts, architectures, and workflows. Visual aids to enhance understanding of complex data engineering concepts and systems.
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Git Repositories: GitHub repositories containing useful data engineering tools, libraries, and projects. Discover open-source projects, tools, and resources to accelerate your data engineering workflows.
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Images: Images used in the repository, such as diagrams and illustrations. Visual assets to supplement your learning and understanding of data engineering topics.
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Interview Questions: Common interview questions for data engineering roles along with answers and explanations. Prepare for data engineering interviews with a curated collection of interview questions and solutions.
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Landscape: Overview of the data engineering landscape, including emerging trends and technologies. Gain insights into the evolving landscape of data engineering, including emerging technologies and industry trends.
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Learning Path: Step-by-step learning paths and roadmaps for becoming a proficient data engineer. Guided learning paths to help you progress from a beginner to an expert in the field of data engineering.
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Practical Projects: Hands-on projects and exercises for applying data engineering skills in real-world scenarios. Practice and apply your data engineering skills through hands-on projects and practical exercises.
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Tools: Tools and software used in data engineering for data processing, ETL, visualization, and more. Explore a comprehensive list of tools and software commonly used in data engineering projects.
This repository is licensed under the MIT License.
e15f099b14dfa7e93c43c38676fe3000f4b2bf4e #data-engineering